Roadway safety has been explored numerous times as an effort to identify problems and implement countermeasures to improve conditions. A clear understanding of the causes of crashes is not yet available; therefore, factors that may be related to crash occurrence are analyzed. Prior studies most often focus on the physical design characteristics of the transportation infrastructure, vehicle characteristics and exposure as factors influencing crash frequency and severity. Socio-demographic characteristics are considered in a few studies, but they are typically limited to gender, age, and race or ethnicity. This study builds upon previous studies and explores factors related to vehicle characteristics, physical infrastructure, and environmental conditions all in a socio-demographic and cultural context, as explanatory factors for differential crash frequency and severity across regions. Data from 2009 to 2013 for each county in Alabama are evaluated in this analysis. Three groups of study are identified based on income level: low, medium, and high income levels, and analyzed in relation to crash rates and fatality rates. Results of the analysis show that the low income group has lower crash rates than the higher income group, but the low income group has higher fatality rates than the higher income group. This study applies charts and GIS maps to evaluate the extent to which characteristics such as age, race, gender, driver license status, employment status, alcohol test results, speed at impact, safety equipment, lighting conditions, road surface type, environmental contributing circumstances, curvature and grade, primary contributing circumstance, EMS response delay, police arrival delay, health insurance coverage, and educational level might explain the differential safety performance between income levels. This study is a description of safety in Alabama counties and provides information to create research questions related to the topic; the authors understand that the results are not developed at the level to implement policies and data should be further analyzed in the future through statistical methods.